铁道运输与经济2023，Vol.45Issue(12)：57-64,8.DOI:10.16668/j.cnki.issn.1003-1421.2023.12.08
基于突发公共卫生事件影响下的铁路客流量恢复率预测研究
Prediction of Railway Passenger Flow Recovery Rate under the Influence of Public Health Emergencies
摘要
Abstract
Since 2020,the influence of random and regional COVID-19 outbreaks on railway passenger flow cannot be ignored.Based on the XGBoost algorithm,this paper built a prediction model for the recovery rate of inter-city railway passenger flow and employed the recovery rate as the prediction target to quantitatively analyze the pandemic severity and change laws of passenger flow.It provides references for passenger flow prediction and auxiliary decision-making and is of significance for reducing the revenue loss of railway passenger transport.The model conducted a principal component analysis based on attributes including the railway mileage among cities of level II and above,passenger flow among cities,geographical and railway distribution characteristics,and the influencing degree of the pandemic,and dimension compression was performed to obtain new input variables.Additionally,the model selected random data sampling in 2021,divided it into training sets and test sets,adopted five-fold cross-validation,and conducted parameter searches based on the grid search method to obtain the optimal model parameters and predict the recovery rate of the test set.Experiments show that the prediction accuracy of the XGBoost algorithm is higher than that of Naive Bayesian and LightGBM algorithms.关键词
XGBoost/恢复率/疫情影响/客流等级/主成分分析Key words
XGBoost/Recovery Rate/Influence of Pandemic/Passenger Flow Level/Principal Component Analysis分类
交通工程引用本文复制引用
周明杉,卫铮铮,李聚宝..基于突发公共卫生事件影响下的铁路客流量恢复率预测研究[J].铁道运输与经济,2023,45(12):57-64,8.基金项目
中国国家铁路集团有限公司科技研究开发计划课题(P2021X009) （P2021X009）
京沪高速铁路股份有限公司科技研究项目(京沪科研-2022-7) （京沪科研-2022-7）
